Experimental Design and Data Analysis – Bali 2010

Title: Experimental Design & Data Analysis – Bali 2010

Instructions for participants: here

Registration: Registration is Closed

Type: Workshop

Venue: Bali Botanic Garden

Dates: 12-16 July 2010 (5 days)

Organiser: Association for Tropical Biology & Conservation Asia-Pacific chapter and University of Indonesia

Sponsors: Asia-Pacific Network for Global Change and Xishuangbanna Tropical Botanic Garden

Fellowships: Travel awards and fellowships to cover the course fees are available for participants from lower-income countries in the Asia-Pacific region. Preference will be given to participants who are presenting a paper (oral or poster) at the Association for Tropical Biology & Conservation meeting (19-23 Jul) in Bali. A limited number of fellowships to cover the conference fees are also available.

Fees: $150 (including transport from airport, accommodation and food)

About: The ATBC Asia-Pacific chapter is running two courses at the above workshop, which is being held in conjunction with the ATBC Bali 2010 meeting. The Introductory course is targeted at post-graduate level participants who have received only very basic training in statistics. It will introduce basic concepts in experimental design and sampling and how to analyse data using traditional modeling methods, including ANOVA and regression. The Advanced course assumes participants are familiar with these basic methods and will introduce maximum likelihood approaches, more advance GLMs and multivariate methods. Please see the course outlines given below. Practicals for both courses will be conducted in R – a free opensource statistical computing program – and will provide an introduction to its use. Participants are required to bring their own laptop computers.

Please see the detailed schedule of topics for both courses below.

Introductory course
DAY 1 Lesson 1 Scientific methodology
Lesson 2 Regression and ANOVA
Practical 1 Regression and ANOVA by hand
Lesson 3 Multi-factor models
Practical 2 Regression and ANOVA in R
DAY 2 Lesson 4 Assumptions of parametric models
Lesson 5 Transformations
Lesson 6 Non-parametric rank tests
Practical 3 Non-parametric rank tests
Practical 4 Graphs in R
DAY 3 Lesson 7, 8 General linear models and dummy variables
Lesson 9 Model fit and simplification
Practical 5 Multi-factor models
Lesson 10 Sampling
Practical 6 Sampling
DAY 4 Lesson 11 Experimental design
Lesson 12 Single factor designs
Lesson 13 Nested designs and variance component analysis
Practical 7 Single factor designs
DAY 5 Lesson 14 Multi-factor designs
Practical 8 Multi-factor models
Lesson 15 Introduction to generalised linear models
Lesson 16 Count data
Lesson 17 Binary and proportion data
Practical 9 Generalised linear models

Advanced course
DAY 1 Lesson 1 Framework for ecological modeling: Classical frequentist vs. likelihood approaches
Practical 1 Introduction to R
Data import, Data frames and matrices, Checking data
DAY 2 Lesson 2 Maximum likelihood estimation
Practical 2 Exploratory data analysis with R
Practical 3 Introduction to R II
Classical anova and regression, checks for violations and interpreting results with continuous and categorial variables
DAY 3 Lesson 3 Introduction to GLM, GLM for count data
Practical 4 GLM for count data
Lesson 4 GLM for binary and proportion data
Practical 5 GLM for binary and proportion data
DAY 4 Lesson 5 Model selection procedures
Lesson 6 Mixed effects models
Practical 6 Mixed effects models
DAY 5 Lesson 7 Introduction to multivariate analysis
Practical 7 Ordination methods
Lesson 8 Methods for community datasets
Practical 8 Variation partitioning